Two advancements in technology over the last few years have resulted in a potential opportunity for investors to begin working in a smarter, more profitable way.

These two technologies are Big Data and Machine Learning (ML). Individually they are useful technologies. Combined, they are greater than the sum of their parts, and could entirely change the way we make investment decisions. And research suggests the value propositions of Big Data technologies for the financial services market segment will grow exponentially – PWC finds that “by 2020 there will be 20 times more usable data than today”.

This short article aims to give an overview of how the two technologies combine into a powerful tool that large financial institutions can use to operate more effectively.

What is Driving the Adoption of ML in Investment Analysis?

The real driver behind the adoption of Machine Learning in the investment sector is the mature state of Big Data tech. Just 5 years ago Big Data was in its infancy. Although it was apparent that Big Data was going to revolutionize the way we capture, manage and store data, much was still theoretical. Fast forward to today and Big Data is a mature technology being leveraged by thousands of companies working in a diverse group of markets. Let’s take a look at two things which enabled the Big Data revolution:

The exponential increase in the amount of data that we capture and store. These huge data repositories contain valuable insights that just need to be extracted.

All this data needs to be stored somewhere, as well as analyzed. The cost of computing power and data storage has dropped significantly in the last few years, and this is a trend which is continuing. The cost of ownership of data is dropping almost daily.

Yet this is only half of the equation. All of this Big Data is only useful if we can find ways to extract the insights it contains. This means we need advanced analytics techniques that can interrogate large datasets efficiently. This is where Machine Learning comes in. ML has now advanced to such a stage that it can truly begin to extract tangible value from the masses of Big Data that investors have been accruing for some years now.

So, to summarize, the maturity of Big Data technology and the growing capabilities of enterprise-grade ML applications have presented investors with something new. A combination of technologies that could entirely reshape the way they make investment decisions.

How Does ML Help with Making Intelligent Investment Decisions?

The effect that AI will have upon investment will be huge. This is a complete paradigm shift in how large datasets are analyzed and investment opportunities are uncovered. Traditional data analytics will be entirely replaced. Gartner’s Vice President Nigel Rayner refers to AI as a “key differentiating factor in finance systems”, highlighting the technology’s transformative potential in the financial industry segment.

Additionally, as investment houses begin to rely more upon agile, alternative datasets rather than static data such as quarterly reports, investment opportunities will be highlighted much more rapidly. The entire investment market, be it stocks, FOREX, precious metals etc. will become much more responsive.

Furthermore, there will exist a new market, in the form of dataset collectors that will be able to sell on these unique investment focused datasets to investment houses.

New Data Analytics Techniques Enabled by ML

The single most disruptive change that Big Data combined with ML will deliver to companies that need to make investment decisions, can be found in the source of data that will now be actionable.

Historically, investors use financial data to try and uncover trends. With the Big Data and ML model, we can begin bringing in additional data sources such as consumer spending habits (through payment processor records), consumer attitude (via social networking sites), and advanced data sources such as satellite imagery. Being able to analyze this kind of alternative data is going to completely change the investment landscape.

The Four Models of AI

In recent years, as multiple tech vendors have begun to work towards delivering narrow focus A.I. driven tools, four key models for the integration of AI into the enterprise have been theorized, and these can be defined as:

Autonomy advisor – here we have the AI working in a fully advisory role. It will work autonomously, performing analysis, but will not action Instead, human oversight is in place to action any investment recommendations that the AI platform comes up with.

Autonomous outsourcer – in this model, the AI is responsible for bringing in external data and resources in order to facilitate the actioning of an investment opportunity. In this model, a human layer that makes the final decisions is in place.

Challenged autonomous employee – in this model, the AI is tasked with running a narrow range of tasks as an expert. This “expert employee” is closely monitored to ensure it is carrying out its allotted tasks correctly.

All-in autonomy – in this model the AI is in fire and forget mode. Making investment decisions and acting upon them as it sees fit.

Making a Case for ML as a Driver of Investment Strategy

If we consider the basic overview of how Big Data and Machine Learning are going to completely change the way that investments are made, then we can come up with some key takeaways thus:

Investors will need to embrace alternative, less traditional data sources in order to gain an edge in their investment strategy.

Even if this technology is not adopted immediately, all people involved in the investment decision-making process will need to understand them sooner rather than later.

Every type of investor is going to need to begin leveraging Big Data and Machine Learning to maintain parity with their competitors.

Despite how “clever” these technologies seem to be, they will still need human supervision. They will be more like an expert investment analyst working at light speed instead of whizz kid investors growing their own portfolio. In fact, AI is set to create more jobs than it will take away according to another Gartner research that finds 2.3 million AI related jobs will be added to the market by the year 2020.

These few takeaways combine to tell anyone involved in financial investment that it is time to wake up and become informed. Failure to adopt these technologies will result in a major loss of profit. Put simply, traditional data analytics will not be able to compete at all with Big Data and Machine Learning once this becomes a mature combined technology. It comes to no surprise that over 70 percent of the financial organizations operating at a global level are already exploring Big Data and predictive analytics initiatives according to a recent Accenture report.

Justin Chan

Dr Justin S P Chan has a passion for clarity and synergy - seeing through the complexity of the intersecting spheres of technology, finance, innovation and social dynamics, to enable game-changing collaborations between entrepreneurs and innovative opportunities.
Combining the vision of a true inventor and entrepreneur with his data-driven, evidence-based approach to investment, Justin also co-founded OCIM and serves as Chief Investment Officer for its fund management platform. Within OCIM, He co-manages OC Horizon Fintech, a transformational hedge fund, where he blends real applications, expertise and future-awareness into truly exceptional investment performance.
Justin gains inspiration for these projects from his global network of contacts in investment and fintech communities, where he stays on the pulse of fast-moving conversations and trends affecting global markets and emerging technologies. Justin can be reached at justinchan@datadriveninvestor.com.